SoroushVahidi/scidocs huggingface dataset:
The Hitchhiker's Guide to Testing Statistical Significance in Natural Language Processing
(2018)

Statistical significance testing is a standard statistical tool designed to ensure that experimental results are not coincidental. In this opinion/theoretical paper we discuss the role of statistical significance testing in Natural Language Processing (NLP) research. We establish the fundamental concepts of significance testing and discuss the specific aspects of NLP tasks, experimental setups and evaluation measures that affect the choice of significance tests in NLP research. Based on this discussion, we propose a simple practical protocol for statistical significance test selection in NLP setups and accompany this protocol with a brief survey of the most relevant tests. We then survey recent empirical papers published in ACL and TACL during 2017 and show that while our community assigns great value to experimental results, statistical significance testing is often ignored or misused. We conclude with a brief discussion of open issues that should be properly addressed so that this important tool can be applied in NLP research in a statistically sound manner1.
Cross-Media Hashing with Neural Networks
(2014)

Cross-media hashing, which conducts cross-media retrieval by embedding data from different modalities into a common low-dimensional hamming space, has attracted intensive attention in recent years. This is motivated by the facts a) the multi-modal data is widespread, e.g., the web images on Flickr are associated with tags, and b) hashing is an effective technique towards large-scale high-dimensional data processing, which is exactly the situation of cross-media retrieval. Inspired by recent advances in deep learning, we propose a cross-media hashing approach based on multi-modal neural networks. By restricting in the learning objective a) the hash codes for relevant cross-media data being similar, and b) the hash codes being discriminative for predicting the class labels, the learned Hamming space is expected to well capture the cross-media semantic relationships and to be semantically discriminative. The experiments on two real-world data sets show that our approach achieves superior cross-media retrieval performance compared with the state-of-the-art methods.
A HOG-based Real-time and Multi-scale Pedestrian Detector Demonstration System on FPGA
(2018)

Pedestrian detection will play a major role in future driver assistance and autonomous driving. One powerful algorithm in this field uses HOG features to describe the specific properties of pedestrians in images. To determine their locations, features are extracted and classified window-wise from different scales of an input image. The results of the classification are finally merged to remove overlapping detections. The real-time execution of this method requires specific FPGA- or ASIC-architectures. Recent work focused on accelerating the feature extraction and classification. Although merging is an important step in the algorithm, it is only rarely considered in hardware implementations. A reason for that could be its complexity and irregularity that is not trivial to implement in hardware. In this paper, we present a new bottom-up FPGA architecture that maps the full HOG-based algorithm for pedestrian detection including feature extraction, SVM classification, and multi-scale processing in combination with merging. For that purpose, we also propose a new hardware-optimized merging method. The resulting architecture is highly efficient. Additionally, we present an FPGA-based full real-time and multi-scale pedestrian detection demonstration system.
An Efficient Algorithm for Calculating the Exact Hausdorff Distance
(2015)

The Hausdorff distance (HD) between two point sets is a commonly used dissimilarity measure for comparing point sets and image segmentations. Especially when very large point sets are compared using the HD, for example when evaluating magnetic resonance volume segmentations, or when the underlying applications are based on time critical tasks, like motion detection, then the computational complexity of HD algorithms becomes an important issue. In this paper we propose a novel efficient algorithm for computing the exact Hausdorff distance. In a runtime analysis, the proposed algorithm is demonstrated to have nearly-linear complexity. Furthermore, it has efficient performance for large point set sizes as well as for large grid size; performs equally for sparse and dense point sets; and finally it is general without restrictions on the characteristics of the point set. The proposed algorithm is tested against the HD algorithm of the widely used national library of medicine insight segmentation and registration toolkit (ITK) using magnetic resonance volumes with extremely large size. The proposed algorithm outperforms the ITK HD algorithm both in speed and memory required. In an experiment using trajectories from a road network, the proposed algorithm significantly outperforms an HD algorithm based on R-Trees.
Deep Network Cascade for Image Super-resolution
(2014)

In this paper, we propose a new model called deep network cascade (DNC) to gradually upscale low-resolution images layer by layer, each layer with a small scale factor. DNC is a cascade of multiple stacked collaborative local auto-encoders. In each layer of the cascade, non-local self-similarity search is first performed to enhance high-frequency texture details of the partitioned patches in the input image. The enhanced image patches are then input into a collaborative local auto-encoder (CLA) to suppress the noises as well as collaborate the compatibility of the overlapping patches. By closing the loop on non-local self-similarity search and CLA in a cascade layer, we can refine the super-resolution result, which is further fed into next layer until the required image scale. Experiments on image super-resolution demonstrate that the proposed DNC can gradually upscale a low-resolution image with the increase of network layers and achieve more promising results in visual quality as well as quantitative performance.
View invariant human action recognition using histograms of 3D joints
(2012)

In this paper, we present a novel approach for human action recognition with histograms of 3D joint locations (HOJ3D) as a compact representation of postures. We extract the 3D skeletal joint locations from Kinect depth maps using Shotton et al.'s method [6]. The HOJ3D computed from the action depth sequences are reprojected using LDA and then clustered into k posture visual words, which represent the prototypical poses of actions. The temporal evolutions of those visual words are modeled by discrete hidden Markov models (HMMs). In addition, due to the design of our spherical coordinate system and the robust 3D skeleton estimation from Kinect, our method demonstrates significant view invariance on our 3D action dataset. Our dataset is composed of 200 3D sequences of 10 indoor activities performed by 10 individuals in varied views. Our method is real-time and achieves superior results on the challenging 3D action dataset. We also tested our algorithm on the MSR Action 3D dataset and our algorithm outperforms Li et al. [25] on most of the cases.
Sparse solutions to linear inverse problems with multiple measurement vectors
(2004)

We address the problem of finding sparse solutions to an underdetermined system of equations when there are multiple measurement vectors having the same, but unknown, sparsity structure. The single measurement sparse solution problem has been extensively studied in the past. Although known to be NP-hard, many single-measurement suboptimal algorithms have been formulated that have found utility in many different applications. Here, we consider in depth the extension of two classes of algorithms-Matching Pursuit (MP) and FOCal Underdetermined System Solver (FOCUSS)-to the multiple measurement case so that they may be used in applications such as neuromagnetic imaging, where multiple measurement vectors are available, and solutions with a common sparsity structure must be computed. Cost functions appropriate to the multiple measurement problem are developed, and algorithms are derived based on their minimization. A simulation study is conducted on a test-case dictionary to show how the utilization of more than one measurement vector improves the performance of the MP and FOCUSS classes of algorithm, and their performances are compared.
Low-voltage low-power CMOS full adder - Circuits, Devices and Systems, IEE Proceedings-
(2004)

Low-power design of VLSI circuits has been identified as a critical technological need in recent years due to the high demand for portable consumer electronics products. In this regard many innovative designs for basic logic functions using pass transistors and transmission gates have appeared in the literature recently. These designs relied on the intuition and cleverness of the designers, without involving formal design procedures. Hence, a formal design procedure for realising a minimal transistor CMOS pass network XOR-XNOR cell, that is fully compensated for threshold voltage drop in MOS transistors, is presented. This new cell can reliably operate within certain bounds when the power supply voltage is scaled down, as long as due consideration is given to the sizing of the MOS transistors during the initial design step. A low transistor count full adder cell using the new XOR-XNOR cell is also presented.
Associations between online friendship and Internet addiction among adolescents and emerging adults.
(2012)

The past decades have witnessed a dramatic increase in the number of youths using the Internet, especially for communicating with peers. Online activity can widen and strengthen the social networks of adolescents and emerging adults (Subrahmanyam & Smahel, 2011), but it also increases the risk of Internet addiction. Using a framework derived from Griffiths (2000a), this study examined associations between online friendship and Internet addiction in a representative sample (n = 394) of Czech youths ages 12-26 years (M = 18.58). Three different approaches to friendship were identified: exclusively offline, face-to-face oriented, Internet oriented, on the basis of the relative percentages of online and offline associates in participants' friendship networks. The rate of Internet addiction did not differ by age or gender but was associated with communication styles, hours spent online, and friendship approaches. The study revealed that effects between Internet addiction and approaches to friendship may be reciprocal: Being oriented toward having more online friends, preferring online communication, and spending more time online were related to increased risk of Internet addiction; on the other hand, there is an alternative causal explanation that Internet addiction and preference for online communication conditions young people's tendency to seek friendship from people met online.
Constructing a Hermitian Matrix from Its Diagonal Entries and Eigenvalues
(1995)

Given two vectors a R the Schur Horn theorem states that a majorizes if and only if there exists a Hermitian matrix H with eigenvalues and diagonal entries a While the theory is regarded as classical by now the known proof is not constructive To construct a Hermitian matrix from its diagonal entries and eigenvalues therefore becomes an interesting and challenging inverse eigenvalue problem Two algorithms for determining the matrix numerically are proposed in this paper The lift and pro jection method is an iterative method which involves an interesting application of the Wielandt Ho man theorem The projected gradient method is a continuous method which besides its easy implementation o ers a new proof of existence because of its global convergence property
Automatic Feature Engineering for Answer Selection and Extraction
(2013)

This paper proposes a framework for automatically engineering features for two important tasks of question answering: answer sentence selection and answer extraction. We represent question and answer sentence pairs with linguistic structures enriched by semantic information, where the latter is produced by automatic classifiers, e.g., question classifier and Named Entity Recognizer. Tree kernels applied to such structures enable a simple way to generate highly discriminative structural features that combine syntactic and semantic information encoded in the input trees. We conduct experiments on a public benchmark from TREC to compare with previous systems for answer sentence selection and answer extraction. The results show that our models greatly improve on the state of the art, e.g., up to 22% on F1 (relative improvement) for answer extraction, while using no additional resources and no manual feature engineering.
Active SLAM using Model Predictive Control and Attractor based Exploration
(2006)

Active SLAM poses the challenge for an autonomous robot to plan efficient paths simultaneous to the SLAM process. The uncertainties of the robot, map and sensor measurements, and the dynamic and motion constraints need to be considered in the planning process. In this paper, the active SLAM problem is formulated as an optimal trajectory planning problem. A novel technique is introduced that utilises an attractor combined with local planning strategies such as model predictive control (a.k.a. receding horizon) to solve this problem. An attractor provides high level task intentions and incorporates global information about the environment for the local planner, thereby eliminating the need for costly global planning with longer horizons. It is demonstrated that trajectory planning with an attractor results in improved performance over systems that have local planning alone
Domestic food and sustainable design: a study of university student cooking and its impacts
(2013)

In four university student kitchens over twenty-one days, we captured participants' food preparation activity, quantified the greenhouse gas emissions and direct energy connected to the food and cooking, and talked to participants about their food practices. Grounded in this uniquely detailed micro-account, our findings inform sustainable design for cooking and eating at home and quantify the potential impacts. We outline the relation of the impacts to our participants' approaches to everyday food preparation, the organisation of their time, and the role of social meals. Our technique allows evaluation of opportunities for sustainable intervention design: at the appliance, in the digitally-mediated organisation of meals and inventory management, and more broadly in reflecting upon and reshaping diet.
Bayesian Neural Networks for Internet Traffic Classification
(2007)

Internet traffic identification is an important tool for network management. It allows operators to better predict future traffic matrices and demands, security personnel to detect anomalous behavior, and researchers to develop more realistic traffic models. We present here a traffic classifier that can achieve a high accuracy across a range of application types without any source or destination host-address or port information. We use supervised machine learning based on a Bayesian trained neural network. Though our technique uses training data with categories derived from packet content, training and testing were done using features derived from packet streams consisting of one or more packet headers. By providing classification without access to the contents of packets, our technique offers wider application than methods that require full packet/payloads for classification. This is a powerful advantage, using samples of classified traffic to permit the categorization of traffic based only upon commonly available information
A VLSI Implementation of an Analog Neural Network Suited for Genetic Algorithms
(2001)

The usefulness of an artificial analog neural network is closely bound to its trainability. This paper introduces a new analog neural network architecture using weights determined by a genetic algorithm. The first VLSI implementation presented in this paper achieves 200 giga connections per second with 4096 synapses on less than 1 mm silicon area. Since the training can be done at the full speed of the network, several hundred individuals per second can be tested by the genetic algorithm. This makes it feasible to tackle problems that require large multi-layered networks.
Relationship Banking : What Do We Know ?
(2000)

This paper briefly reviews the contemporary literature on relationship banking. We start out with a discussion of the raison d’ˆ etre of banks in the context of the financial intermediation literature. From there we discuss how relationship banking fits into the core economic services provided by banks and point at its costs and benefits. This leads to an examination of the interrelationship between the competitive environment and relationship banking as well as a discussion of the empirical evidence. Journal of Economic LiteratureClassification Numbers: G20, G21, L10. C © 2000 Academic Press
Microalgae as a raw material for biofuels production
(2008)

Biofuels demand is unquestionable in order to reduce gaseous emissions (fossil CO2, nitrogen and sulfur oxides) and their purported greenhouse, climatic changes and global warming effects, to face the frequent oil supply crises, as a way to help non-fossil fuel producer countries to reduce energy dependence, contributing to security of supply, promoting environmental sustainability and meeting the EU target of at least of 10% biofuels in the transport sector by 2020. Biodiesel is usually produced from oleaginous crops, such as rapeseed, soybean, sunflower and palm. However, the use of microalgae can be a suitable alternative feedstock for next generation biofuels because certain species contain high amounts of oil, which could be extracted, processed and refined into transportation fuels, using currently available technology; they have fast growth rate, permit the use of non-arable land and non-potable water, use far less water and do not displace food crops cultures; their production is not seasonal and they can be harvested daily. The screening of microalgae (Chlorella vulgaris, Spirulina maxima, Nannochloropsis sp., Neochloris oleabundans, Scenedesmus obliquus and Dunaliella tertiolecta) was done in order to choose the best one(s), in terms of quantity and quality as oil source for biofuel production. Neochloris oleabundans (fresh water microalga) and Nannochloropsis sp. (marine microalga) proved to be suitable as raw materials for biofuel production, due to their high oil content (29.0 and 28.7%, respectively). Both microalgae, when grown under nitrogen shortage, show a great increase (~50%) in oil quantity. If the purpose is to produce biodiesel only from one species, Scenedesmus obliquus presents the most adequate fatty acid profile, namely in terms of linolenic and other polyunsaturated fatty acids. However, the microalgae Neochloris oleabundans, Nannochloropsis sp. and Dunaliella tertiolecta can also be used if associated with other microalgal oils and/or vegetable oils.
Error-Correcting Codes for Semiconductor Memory Applications: A State-of-the-Art Review
(1984)

This paper presents a state-of-the-art review of error-correcting codes for computer semiconductor memory applications. The construction of four classes of error-correcting codes appropriate for semiconductor memory designs is described, and for each class of codes the number of check bits required for commonly used data lengths is provided. The implementation aspects of error correction and error detection are also discussed, and certain algorithms useful in extending the error-correcting capability for the correction of soft errors such as a-particle-induced errors are examined in some detail.
Smarter Presentations: Exploiting Homography in Camera-Projector Systems
(2001)

Standard presentation systems consisting of a laptop connected to a projector suffer from two problems: (1) the projected image appears distorted (keystoned) unless the projector is precisely aligned to the projection screen; (2) the speaker is forced to interact with the computer rather than the audience. This paper shows how the addition of an uncalibrated camera, aimed at the screen, solves both problems. Although the locations, orientations and optical parameters of the camera and projector are unknown, the projector-camera system calibrates itself by exploiting the homography between the projected slide and the camera image. Significant improvements are possible over passively calibrating systems since the projector actively manipulates the environment by placing feature points into the scene. For instance, using a low-resolution (160x120) camera, we can achieve an accuracy of ±3 pixels in a 1024x768 presentation slide. The camera-projector system infers models for the projector-to-camera and projector-to-screen mappings in order to provide two major benefits. First, images sent to the projector are pre-warped in such a way that the distortions induced by the arbitrary projector-screen geometry are precisely negated. This enables projectors to be mounted anywhere in the environment – for instance, at the side of the room, where the speaker is less likely to cast shadows on the screen, and where the projector does not occlude the audience’s view. Second, the system detects the position of the user’s laser pointer dot in the camera image at 20Hz, allowing the laser pointer to emulate the pointing actions of a mouse. This enables the user to activate virtual buttons in the presentation (such as “next slide”) and draw on the projected image. The camera-assisted presentation system requires no special hardware (aside from the cheap camera) Rahul Sukthankar (rahuls@cs.cmu.edu) is now affiliated with Compaq Cambridge Research Lab and Carnegie Mellon; Robert Stockton and Matthew Mullin ({rstock,mmullin}@whizbang.com) are now with WhizBang! Labs. and runs on a standard laptop as a Java application. It is now used by the authors for all of their conference presentations.
Scholarly paper recommendation via user's recent research interests
(2010)

We examine the effect of modeling a researcher's past works in recommending scholarly papers to the researcher. Our hypothesis is that an author's published works constitute a clean signal of the latent interests of a researcher. A key part of our model is to enhance the profile derived directly from past works with information coming from the past works' referenced papers as well as papers that cite the work. In our experiments, we differentiate between junior researchers that have only published one paper and senior researchers that have multiple publications. We show that filtering these sources of information is advantageous -- when we additionally prune noisy citations, referenced papers and publication history, we achieve statistically significant higher levels of recommendation accuracy.
The role of the inferior frontal junction area in cognitive control
(2005)

Cognitive control processes refer to our ability to coordinate thoughts and actions in accordance with internal goals. In the fronto-lateral cortex such processes have been primarily related to mid-dorsolateral prefrontal cortex (mid-DLPFC). However, recent brain-imaging and meta-analytic studies suggest that a region located more posterior in the fronto-lateral cortex plays a pivotal role in cognitive control as well. This region has been termed the inferior frontal junction area and can be functionally and structurally distinguished from mid-DLPFC.